




"""simple docstring"""




import re
import tempfile
from pathlib import Path

import pytest
import yaml

from datasets.utils.readme import ReadMe


# @pytest.fixture
# def example_yaml_structure():

a_        =       yaml.safe_load(
    '\\nname: ""\nallow_empty: false\nallow_empty_text: true\nsubsections:\n  - name: "Dataset Card for X" # First-level markdown heading\n    allow_empty: false\n    allow_empty_text: true\n    subsections:\n      - name: "Table of Contents"\n        allow_empty: false\n        allow_empty_text: false\n        subsections: null\n      - name: "Dataset Description"\n        allow_empty: false\n        allow_empty_text: false\n        subsections:\n          - name: "Dataset Summary"\n            allow_empty: false\n            allow_empty_text: false\n            subsections: null\n          - name: "Supported Tasks and Leaderboards"\n            allow_empty: true\n            allow_empty_text: true\n            subsections: null\n          - name: Languages\n            allow_empty: false\n            allow_empty_text: true\n            subsections: null\n'
)


a_        =       {
    'name': 'root',
    'text': '',
    'is_empty_text': True,
    'subsections': [
        {
            'name': 'Dataset Card for My Dataset',
            'text': '',
            'is_empty_text': True,
            'subsections': [
                {'name': 'Table of Contents', 'text': 'Some text here.', 'is_empty_text': False, 'subsections': []},
                {
                    'name': 'Dataset Description',
                    'text': 'Some text here.',
                    'is_empty_text': False,
                    'subsections': [
                        {
                            'name': 'Dataset Summary',
                            'text': 'Some text here.',
                            'is_empty_text': False,
                            'subsections': [],
                        },
                        {
                            'name': 'Supported Tasks and Leaderboards',
                            'text': '',
                            'is_empty_text': True,
                            'subsections': [],
                        },
                        {'name': 'Languages', 'text': 'Language Text', 'is_empty_text': False, 'subsections': []},
                    ],
                },
            ],
        }
    ],
}


a_        =       '\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n'


a_        =       '\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n#### Extra Ignored Subsection\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n'

a_        =       {
    'name': 'root',
    'text': '',
    'is_empty_text': True,
    'subsections': [
        {
            'name': 'Dataset Card for My Dataset',
            'text': '',
            'is_empty_text': True,
            'subsections': [
                {'name': 'Table of Contents', 'text': 'Some text here.', 'is_empty_text': False, 'subsections': []},
                {
                    'name': 'Dataset Description',
                    'text': 'Some text here.',
                    'is_empty_text': False,
                    'subsections': [
                        {
                            'name': 'Dataset Summary',
                            'text': 'Some text here.',
                            'is_empty_text': False,
                            'subsections': [
                                {
                                    'name': 'Extra Ignored Subsection',
                                    'text': '',
                                    'is_empty_text': True,
                                    'subsections': [],
                                }
                            ],
                        },
                        {
                            'name': 'Supported Tasks and Leaderboards',
                            'text': '',
                            'is_empty_text': True,
                            'subsections': [],
                        },
                        {'name': 'Languages', 'text': 'Language Text', 'is_empty_text': False, 'subsections': []},
                    ],
                },
            ],
        }
    ],
}

a_        =       '\\n---\n---\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n'

a_        =       (
    'The following issues were found for the README at `{path}`:\n-\tEmpty YAML markers are present in the README.'
)

a_        =       '\\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n'

a_        =       (
    'The following issues were found for the README at `{path}`:\n-\tNo YAML markers are present in the README.'
)

a_        =       '\\n---\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n'

a_        =       'The following issues were found for the README at `{path}`:\n-\tOnly the start of YAML tags present in the README.'

a_        =       '\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n'
a_        =       'The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Summary` but it is empty.\n-\tExpected some text in section `Dataset Summary` but it is empty (text in subsections are ignored).'


a_        =       '\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n'
a_        =       'The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Card for My Dataset` but it is empty.\n-\tSection `Dataset Card for My Dataset` expected the following subsections: `Table of Contents`, `Dataset Description`. Found \'None\'.'

a_        =       '\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Languages\nLanguage Text\n'

a_        =       'The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`.'


a_        =       '\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\n'

a_        =       'The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Languages` but it is empty.'

a_        =       '\\n---\nlanguage:\n- zh\n- en\n---\n\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n'
a_        =       'The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.'

a_        =       '\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n# Dataset Card My Dataset\n'

a_        =       'The following issues were found for the README at `{path}`:\n-\tThe README has several first-level headings: `Dataset Card for My Dataset`, `Dataset Card My Dataset`. Only one heading is expected. Skipping further validation for this README.'

a_        =       '\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n'

a_        =       'The following issues were found for the README at `{path}`:\n-\tNo first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README.'

a_        =       ''

a_        =       'The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.\n-\tNo YAML markers are present in the README.'

a_        =       '\\n---\nlanguage:\n- zh\n- en\n---\n\n# Dataset Card for My Dataset\n# Dataset Card for My Dataset\n## Table of Contents\nSome text here.\n## Dataset Description\nSome text here.\n### Dataset Summary\nSome text here.\n### Supported Tasks and Leaderboards\n### Languages\nLanguage Text\n'

a_        =       'The following issues were found while parsing the README at `{path}`:\n-\tMultiple sections with the same heading `Dataset Card for My Dataset` have been found. Please keep only one of these sections.'







@pytest.mark.parametrize(
    '''readme_md, expected_dict'''	,				[
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ]	,				)
def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	):
			assert ReadMe.from_string(__UpperCamelCase	,				__UpperCamelCase	).to_dict() == expected_dict







@pytest.mark.parametrize(
    '''readme_md, expected_error'''	,				[
        (README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
        (README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
        (README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
        (README_EMPTY, EXPECTED_ERROR_README_EMPTY),
        (README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
        (README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
        (README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
        (README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
        (README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
        (README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
        (README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
    ]	,				)
def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	):
			with pytest.raises(__UpperCamelCase	,				match=re.escape(expected_error.format(path='''root'''	)	)	):
						__lowercase			:  Any								=       ReadMe.from_string(__UpperCamelCase	,				__UpperCamelCase	)
						readme.validate()







@pytest.mark.parametrize(
    '''readme_md, expected_error'''	,				[
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ]	,				)
def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	):
			with pytest.raises(__UpperCamelCase	,				match=re.escape(expected_error.format(path='''root'''	)	)	):
						ReadMe.from_string(__UpperCamelCase	,				__UpperCamelCase	)







@pytest.mark.parametrize(
    '''readme_md,'''	,				[
        (README_MULTIPLE_SAME_HEADING_1),
    ]	,				)
def      __UpperCAmelCase     ( __UpperCamelCase	):
			ReadMe.from_string(__UpperCamelCase	,				__UpperCamelCase	,				suppress_parsing_errors=__UpperCamelCase	)







@pytest.mark.parametrize(
    '''readme_md, expected_dict'''	,				[
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ]	,				)
def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	):
			with tempfile.TemporaryDirectory() as tmp_dir:
						__lowercase			:  List[Any]								=       Path(__UpperCamelCase	) / '''README.md'''
						with open(__UpperCamelCase	,				'''w+'''	) as readme_file:
									readme_file.write(__UpperCamelCase	)
						__lowercase			:  List[Any]								=       ReadMe.from_readme(__UpperCamelCase	,				__UpperCamelCase	).to_dict()
						assert out["name"] == path
						assert out["text"] == ""
						assert out["is_empty_text"]
						assert out["subsections"] == expected_dict["subsections"]







@pytest.mark.parametrize(
    '''readme_md, expected_error'''	,				[
        (README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
        (README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
        (README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
        (README_EMPTY, EXPECTED_ERROR_README_EMPTY),
        (README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
        (README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
        (README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
        (README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
        (README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
        (README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
        (README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
    ]	,				)
def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	):
			with tempfile.TemporaryDirectory() as tmp_dir:
						__lowercase			:  Any								=       Path(__UpperCamelCase	) / '''README.md'''
						with open(__UpperCamelCase	,				'''w+'''	) as readme_file:
									readme_file.write(__UpperCamelCase	)
						__lowercase			:  Any								=       expected_error.format(path=__UpperCamelCase	)
						with pytest.raises(__UpperCamelCase	,				match=re.escape(__UpperCamelCase	)	):
									__lowercase			:  List[str]								=       ReadMe.from_readme(__UpperCamelCase	,				__UpperCamelCase	)
									readme.validate()







@pytest.mark.parametrize(
    '''readme_md, expected_error'''	,				[
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ]	,				)
def      __UpperCAmelCase     ( __UpperCamelCase	,				__UpperCamelCase	):
			with tempfile.TemporaryDirectory() as tmp_dir:
						__lowercase			:  List[str]								=       Path(__UpperCamelCase	) / '''README.md'''
						with open(__UpperCamelCase	,				'''w+'''	) as readme_file:
									readme_file.write(__UpperCamelCase	)
						__lowercase			:  Union[str, Any]								=       expected_error.format(path=__UpperCamelCase	)
						with pytest.raises(__UpperCamelCase	,				match=re.escape(__UpperCamelCase	)	):
									ReadMe.from_readme(__UpperCamelCase	,				__UpperCamelCase	)







@pytest.mark.parametrize(
    '''readme_md,'''	,				[
        (README_MULTIPLE_SAME_HEADING_1),
    ]	,				)
def      __UpperCAmelCase     ( __UpperCamelCase	):
			with tempfile.TemporaryDirectory() as tmp_dir:
						__lowercase			:  List[Any]								=       Path(__UpperCamelCase	) / '''README.md'''
						with open(__UpperCamelCase	,				'''w+'''	) as readme_file:
									readme_file.write(__UpperCamelCase	)
						ReadMe.from_readme(__UpperCamelCase	,				__UpperCamelCase	,				suppress_parsing_errors=__UpperCamelCase	)





"""simple docstring"""


from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)



@add_end_docstrings(_lowerCAmelCase						)
class 	__a	(   _lowerCAmelCase						):






       def __init__(	self			:				str							,   **UpperCAmelCase_			:				List[str]      )->	Tuple:







              """simple docstring"""
              super().__init__(**UpperCAmelCase_      )
              requires_backends(self							,   "vision"      )
              requires_backends(self							,   "torch"      )

              if self.framework != "pt":
                     raise ValueError(f"The {self.__class__} is only available in PyTorch."      )

              self.check_model_type(UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Tuple							,   **UpperCAmelCase_			:				int      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		{}
              UpperCamelCase       			=		{}
              UpperCamelCase       			=		{}
              # preprocess args
              if "points_per_batch" in kwargs:
                     UpperCamelCase       			=		kwargs["points_per_batch"]
              if "points_per_crop" in kwargs:
                     UpperCamelCase       			=		kwargs["points_per_crop"]
              if "crops_n_layers" in kwargs:
                     UpperCamelCase       			=		kwargs["crops_n_layers"]
              if "crop_overlap_ratio" in kwargs:
                     UpperCamelCase       			=		kwargs["crop_overlap_ratio"]
              if "crop_n_points_downscale_factor" in kwargs:
                     UpperCamelCase       			=		kwargs["crop_n_points_downscale_factor"]
              # postprocess args
              if "pred_iou_thresh" in kwargs:
                     UpperCamelCase       			=		kwargs["pred_iou_thresh"]
              if "stability_score_offset" in kwargs:
                     UpperCamelCase       			=		kwargs["stability_score_offset"]
              if "mask_threshold" in kwargs:
                     UpperCamelCase       			=		kwargs["mask_threshold"]
              if "stability_score_thresh" in kwargs:
                     UpperCamelCase       			=		kwargs["stability_score_thresh"]
              if "crops_nms_thresh" in kwargs:
                     UpperCamelCase       			=		kwargs["crops_nms_thresh"]
              if "output_rle_mask" in kwargs:
                     UpperCamelCase       			=		kwargs["output_rle_mask"]
              if "output_bboxes_mask" in kwargs:
                     UpperCamelCase       			=		kwargs["output_bboxes_mask"]
              return preprocess_kwargs, forward_params, postprocess_kwargs






       def __call__(	self			:				Optional[int]							,   UpperCAmelCase_			:				Optional[int]							,   *UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				int=None							,   UpperCAmelCase_			:				Tuple=None							,   **UpperCAmelCase_			:				str      )->	Any:







              """simple docstring"""
              return super().__call__(UpperCAmelCase_							,   *UpperCAmelCase_							,   num_workers=UpperCAmelCase_							,   batch_size=UpperCAmelCase_							,   **UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]							,   UpperCAmelCase_			:				Optional[int]							,   UpperCAmelCase_			:				str=64							,   UpperCAmelCase_			:				int = 0							,   UpperCAmelCase_			:				float = 512 / 1_500							,   UpperCAmelCase_			:				Optional[int] = 32							,   UpperCAmelCase_			:				Optional[int] = 1							,   )->	List[Any]:







              """simple docstring"""
              UpperCamelCase       			=		load_image(UpperCAmelCase_      )
              UpperCamelCase       			=		self.image_processor.size["longest_edge"]
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.image_processor.generate_crop_boxes(
                  UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )
              UpperCamelCase       			=		self.image_processor(images=UpperCAmelCase_							,   return_tensors="pt"      )

              with self.device_placement():
                     if self.framework == "pt":
                            UpperCamelCase       			=		self.get_inference_context()
                            with inference_context():
                                   UpperCamelCase       			=		self._ensure_tensor_on_device(UpperCAmelCase_							,   device=self.device      )
                                   UpperCamelCase       			=		self.model.get_image_embeddings(model_inputs.pop("pixel_values"      )      )
                                   UpperCamelCase       			=		image_embeddings

              UpperCamelCase       			=		grid_points.shape[1]
              UpperCamelCase       			=		points_per_batch if points_per_batch is not None else n_points

              if points_per_batch <= 0:
                     raise ValueError(
                         "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
                         "To return all points at once, set points_per_batch to None"      )

              for i in range(0							,   UpperCAmelCase_							,   UpperCAmelCase_      ):
                     UpperCamelCase       			=		grid_points[:, i : i + points_per_batch, :, :]
                     UpperCamelCase       			=		input_labels[:, i : i + points_per_batch]
                     UpperCamelCase       			=		i == n_points - points_per_batch
                     yield {
                         "input_points": batched_points,
                         "input_labels": labels,
                         "input_boxes": crop_boxes,
                         "is_last": is_last,
                         **model_inputs,
                     }






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				Union[str, Any]							,   UpperCAmelCase_			:				int=0.88							,   UpperCAmelCase_			:				Optional[int]=0.95							,   UpperCAmelCase_			:				Dict=0							,   UpperCAmelCase_			:				int=1							,   )->	Any:







              """simple docstring"""
              UpperCamelCase       			=		model_inputs.pop("input_boxes"      )
              UpperCamelCase       			=		model_inputs.pop("is_last"      )
              UpperCamelCase       			=		model_inputs.pop("original_sizes"      ).tolist()
              UpperCamelCase       			=		model_inputs.pop("reshaped_input_sizes"      ).tolist()

              UpperCamelCase       			=		self.model(**UpperCAmelCase_      )

              # post processing happens here in order to avoid CPU GPU copies of ALL the masks
              UpperCamelCase       			=		model_outputs["pred_masks"]
              UpperCamelCase       			=		self.image_processor.post_process_masks(
                  UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   binarize=UpperCAmelCase_      )
              UpperCamelCase       			=		model_outputs["iou_scores"]
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.image_processor.filter_masks(
                  masks[0]							,   iou_scores[0]							,   original_sizes[0]							,   input_boxes[0]							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   )
              return {
                  "masks": masks,
                  "is_last": is_last,
                  "boxes": boxes,
                  "iou_scores": iou_scores,
              }






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any							,   UpperCAmelCase_			:				Tuple							,   UpperCAmelCase_			:				List[str]=False							,   UpperCAmelCase_			:				str=False							,   UpperCAmelCase_			:				Optional[int]=0.7							,   )->	Dict:







              """simple docstring"""
              UpperCamelCase       			=		[]
              UpperCamelCase       			=		[]
              UpperCamelCase       			=		[]
              for model_output in model_outputs:
                     all_scores.append(model_output.pop("iou_scores"      )      )
                     all_masks.extend(model_output.pop("masks"      )      )
                     all_boxes.append(model_output.pop("boxes"      )      )

              UpperCamelCase       			=		torch.cat(UpperCAmelCase_      )
              UpperCamelCase       			=		torch.cat(UpperCAmelCase_      )
              UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase			,							UpperCamelCase       			=		self.image_processor.post_process_for_mask_generation(
                  UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_							,   UpperCAmelCase_      )

              UpperCamelCase       			=		defaultdict(UpperCAmelCase_      )
              for output in model_outputs:
                     for k, v in output.items():
                            extra[k].append(UpperCAmelCase_      )

              UpperCamelCase       			=		{}
              if output_rle_mask:
                     UpperCamelCase       			=		rle_mask

              if output_bboxes_mask:
                     UpperCamelCase       			=		bounding_boxes

              return {"masks": output_masks, "scores": iou_scores, **optional, **extra}





